Unsupervised Domain Relevance Estimation for Word Sense Disambiguation

Unsupervised Domain Relevance Estimation for Word Sense Disambiguation Alfio Gliozzo and Bernardo Magnini and Carlo Strapparava ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, I-38050 Trento, ITALY {gliozzo, magnini, strappa}@itc.it Abstract This paper presents Domain Relevance Estima- tion (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain cate- gory. We use a pre-defined set of categories (we call them domains) which have been previously as- sociated to WORDNET word senses. Given a cer- tain domain, DRE distinguishes between relevant and non-relevant texts by means of a Gaussian Mix- ture model that describes the frequency distribution of domain words inside a large-scale corpus. Then, an Expectation Maximization algorithm computes the parameters that maximize the likelihood of the model on the empirical data. The correct identification of the domain of the text is a crucial point for Domain Driven Dis- ambiguation, an unsupervised Word Sense Disam- biguation (WSD) methodology that makes use of only domain information. Therefore, DRE has been exploited and evaluated in the context of a WSD task. Results are comparable to those of state-of- the-art unsupervised WSD systems and show that DRE provides an important contribution. 1 Introduction A fundamental issue in text processing and under- standing is the ability to detect the topic (i.e. the do- main) of a text or of a portion of it. Indeed, domain detection allows a number of useful simplifications in text processing applications, such as, for instance, in Word Sense Disambiguation (WSD). In this paper we introduce Domain Relevance Es- timation (DRE) a fully unsupervised technique for domain detection. Roughly speaking, DRE can be viewed as a text categorization (TC) problem (Se- bastiani, 2002), even if we do not approach the problem in the standard supervised setting requir- ing category labeled training data. In fact, recently, unsupervised approaches to TC have received more and more attention in the literature (see for example (Ko and Seo, 2000). We assume a pre-defined set of categories, each defined by means of a list of related terms. We call such categories domains and we consider them as a set of general topics (e.g. SPORT, MEDICINE, POLITICS) that cover the main disciplines and ar- eas of human activity. For each domain, the list of related words is extracted from WORDNET DO- MAINS (Magnini and Cavagli`a, 2000), an extension of WORDNET in which synsets are annotated with domain labels. We have identified about 40 domains (out of 200 present in WORDNET DOMAINS) and we will use them for experiments throughout the pa- per (see Table 1). DRE focuses on the problem of estimating a de- gree of relatedness of a certain text with respect to the domains in WORDNET DOMAINS. The basic idea underlying DRE is to combine the knowledge in WORDNET DOMAINS and a proba- bilistic framework which makes use of a large-scale corpus to induce domain frequency distributions. Specifically, given a certain domain, DRE considers frequency scores for both relevant and non-relevant …

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